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Record W2900906759 · doi:10.1139/juvs-2018-0020

Do Americans differ in their willingness to ride in a driverless bus?

2018· article· en· W2900906759 on OpenAlexvenueno aff
Scott R. Winter, Stephen Rice, Rian Mehta, Nathan Walters, Matthew Pierce, Emily C. Anania, Mattie N. Milner, Natasha Rao

Bibliographic record

VenueJournal of Unmanned Vehicle Systems · 2018
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsSchool busOn boardPsychologyTransport engineeringEngineering

Abstract

fetched live from OpenAlex

The purpose of this study was to examine a person’s willingness to ride (WTR) in an autonomous bus. Across two studies, we presented participants with hypothetical scenarios about riding in a driverless city or inter-city bus. We manipulated who was onboard the bus (participant, romantic partner, or child), the location of the bus (seven different countries), and the type of driver (human or driverless). In Study 1, participants were less willing to ride a driverless city bus compared to one driven by a human driver. In Study 2, participants’ WTR scores were influenced by participant gender, the person on board, and location, with scores dropping dramatically when the bus was located outside of the USA, or when a child was on board. The current data suggest that Americans are not entirely ready for driverless buses, mainly when someone they care about is on board, or the bus is located outside the USA.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.360
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations24
Published2018
Admission routes1
Has abstractyes

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